import os
import sys
import traceback

import numpy as np
import torch
from PIL import Image
from basicsr.utils.download_util import load_file_from_url

import modules.esrgam_model_arch as arch
import modules.images
from modules import shared
from modules import shared, modelloader
from modules.devices import has_mps
from modules.paths import models_path
from modules.shared import opts

model_dir = "ESRGAN"
model_path = os.path.join(models_path, model_dir)
model_url = "https://drive.google.com/u/0/uc?id=1TPrz5QKd8DHHt1k8SRtm6tMiPjz_Qene&export=download"
model_name = "ESRGAN_x4"


def load_model(path: str, name: str):
    global model_path
    global model_url
    global model_dir
    global model_name
    if "http" in path:
        filename = load_file_from_url(url=model_url, model_dir=model_path, file_name="%s.pth" % model_name, progress=True)
    else:
        filename = path
    if not os.path.exists(filename) or filename is None:
        print("Unable to load %s from %s" % (model_dir, filename))
        return None
    print("Loading %s from %s" % (model_dir, filename))
    # this code is adapted from https://github.com/xinntao/ESRGAN
    pretrained_net = torch.load(filename, map_location='cpu' if has_mps else None)
    crt_model = arch.RRDBNet(3, 3, 64, 23, gc=32)

    if 'conv_first.weight' in pretrained_net:
        crt_model.load_state_dict(pretrained_net)
        return crt_model

    if 'model.0.weight' not in pretrained_net:
        is_realesrgan = "params_ema" in pretrained_net and 'body.0.rdb1.conv1.weight' in pretrained_net["params_ema"]
        if is_realesrgan:
            raise Exception("The file is a RealESRGAN model, it can't be used as a ESRGAN model.")
        else:
            raise Exception("The file is not a ESRGAN model.")

    crt_net = crt_model.state_dict()
    load_net_clean = {}
    for k, v in pretrained_net.items():
        if k.startswith('module.'):
            load_net_clean[k[7:]] = v
        else:
            load_net_clean[k] = v
    pretrained_net = load_net_clean

    tbd = []
    for k, v in crt_net.items():
        tbd.append(k)

    # directly copy
    for k, v in crt_net.items():
        if k in pretrained_net and pretrained_net[k].size() == v.size():
            crt_net[k] = pretrained_net[k]
            tbd.remove(k)

    crt_net['conv_first.weight'] = pretrained_net['model.0.weight']
    crt_net['conv_first.bias'] = pretrained_net['model.0.bias']

    for k in tbd.copy():
        if 'RDB' in k:
            ori_k = k.replace('RRDB_trunk.', 'model.1.sub.')
            if '.weight' in k:
                ori_k = ori_k.replace('.weight', '.0.weight')
            elif '.bias' in k:
                ori_k = ori_k.replace('.bias', '.0.bias')
            crt_net[k] = pretrained_net[ori_k]
            tbd.remove(k)

    crt_net['trunk_conv.weight'] = pretrained_net['model.1.sub.23.weight']
    crt_net['trunk_conv.bias'] = pretrained_net['model.1.sub.23.bias']
    crt_net['upconv1.weight'] = pretrained_net['model.3.weight']
    crt_net['upconv1.bias'] = pretrained_net['model.3.bias']
    crt_net['upconv2.weight'] = pretrained_net['model.6.weight']
    crt_net['upconv2.bias'] = pretrained_net['model.6.bias']
    crt_net['HRconv.weight'] = pretrained_net['model.8.weight']
    crt_net['HRconv.bias'] = pretrained_net['model.8.bias']
    crt_net['conv_last.weight'] = pretrained_net['model.10.weight']
    crt_net['conv_last.bias'] = pretrained_net['model.10.bias']

    crt_model.load_state_dict(crt_net)
    crt_model.eval()
    return crt_model

def upscale_without_tiling(model, img):
    img = np.array(img)
    img = img[:, :, ::-1]
    img = np.moveaxis(img, 2, 0) / 255
    img = torch.from_numpy(img).float()
    img = img.unsqueeze(0).to(shared.device)
    with torch.no_grad():
        output = model(img)
    output = output.squeeze().float().cpu().clamp_(0, 1).numpy()
    output = 255. * np.moveaxis(output, 0, 2)
    output = output.astype(np.uint8)
    output = output[:, :, ::-1]
    return Image.fromarray(output, 'RGB')


def esrgan_upscale(model, img):
    if opts.ESRGAN_tile == 0:
        return upscale_without_tiling(model, img)

    grid = modules.images.split_grid(img, opts.ESRGAN_tile, opts.ESRGAN_tile, opts.ESRGAN_tile_overlap)
    newtiles = []
    scale_factor = 1

    for y, h, row in grid.tiles:
        newrow = []
        for tiledata in row:
            x, w, tile = tiledata

            output = upscale_without_tiling(model, tile)
            scale_factor = output.width // tile.width

            newrow.append([x * scale_factor, w * scale_factor, output])
        newtiles.append([y * scale_factor, h * scale_factor, newrow])

    newgrid = modules.images.Grid(newtiles, grid.tile_w * scale_factor, grid.tile_h * scale_factor, grid.image_w * scale_factor, grid.image_h * scale_factor, grid.overlap * scale_factor)
    output = modules.images.combine_grid(newgrid)
    return output


class UpscalerESRGAN(modules.images.Upscaler):
    def __init__(self, filename, title):
        self.name = title
        self.filename = filename

    def do_upscale(self, img):
        model = load_model(self.filename, self.name)
        if model is None:
            return img
        model.to(shared.device)
        img = esrgan_upscale(model, img)
        return img


def setup_model(dirname):
    global model_path
    global model_name
    if not os.path.exists(model_path):
        os.makedirs(model_path)

    model_paths = modelloader.load_models(model_path, command_path=dirname, ext_filter=[".pt", ".pth"])
    if len(model_paths) == 0:
        modules.shared.sd_upscalers.append(UpscalerESRGAN(model_url, model_name))
    for file in model_paths:
        name = modelloader.friendly_name(file)
        try:
            modules.shared.sd_upscalers.append(UpscalerESRGAN(file, name))
        except Exception:
            print(f"Error loading ESRGAN model: {file}", file=sys.stderr)
            print(traceback.format_exc(), file=sys.stderr)